RAG Text Chunker — 為 RAG 切分文字，並建立 Ollama /api/embed 請求

請在能連到 Ollama 的機器上執行。回應會依此順序，為每個區塊各給一個向量。

curl http://localhost:11434/api/embed -d '{
  "model": "embeddinggemma",
  "input": [
    "Embeddings turn text into numeric vectors. You can store them in a vector database and search them with cosine similarity, or use them in a RAG pipeline.\n\nThe length of each vector depends on the embedding model. Use the same model when you index your documents and when you embed a question.\n\n",
    "Ollama'\''s /api/embed endpoint takes a single string or an array of strings. By default, input that is longer than the model'\''s context window is truncated. Set truncate to false to get an error instead.\n\n",
    "Split long documents into smaller chunks before you embed them, so that each chunk fits in the context window and a search can return the part that answers the question."
  ],
  "truncate": false
}'

區塊

共 3 個區塊，最長的有 294 個字元。

1. #0 · 294 個字元 · 起始於偏移量 0

   Embeddings turn text into numeric vectors. You can store them in a vector database and search them with cosine similarity, or use them in a RAG pipeline. The length of each vector depends on the embedding model. Use the same model when you index your documents and when you embed a question.

2. #1 · 202 個字元 · 起始於偏移量 294

   Ollama's /api/embed endpoint takes a single string or an array of strings. By default, input that is longer than the model's context window is truncated. Set truncate to false to get an error instead.

3. #2 · 169 個字元 · 起始於偏移量 496

   Split long documents into smaller chunks before you embed them, so that each chunk fits in the context window and a search can return the part that answers the question.

{
  "model": "embeddinggemma",
  "input": [
    "Embeddings turn text into numeric vectors. You can store them in a vector database and search them with cosine similarity, or use them in a RAG pipeline.\n\nThe length of each vector depends on the embedding model. Use the same model when you index your documents and when you embed a question.\n\n",
    "Ollama's /api/embed endpoint takes a single string or an array of strings. By default, input that is longer than the model's context window is truncated. Set truncate to false to get an error instead.\n\n",
    "Split long documents into smaller chunks before you embed them, so that each chunk fits in the context window and a search can return the part that answers the question."
  ],
  "truncate": false
}